I was previously using VSCode then switched to Zed with PowerShell. Although I have a Mac, my main working machine is Windows so I can never use tools like Ghostty, Tmux, etc. I decided to build my own terminal based IDE (or is it IDE?) in Rust, specifically designed for coding CLIs to work in parallel.

My favorite features are:

• Custom layout: Define your layout and you can drag the header to move it to wherever you like

• Notifications: No need to explain this, when you have multiple sessions running, you need it.

• Session persistence: If you close Codirigent without closing your session, next time you reopen it, it will resume to the last session with the same permission settings!

• Clipboard: Finally can copy and paste images directly into the terminal!

• File tree: You can just double click files to open them in your default IDE for review. You can also right click on files to insert the file path directly instead of typing the file name each time.

• Session menus: You can rename your session and group them for easy visual hints.

I m always on the lookout for those aha moments where a product nails the timing, design, or simplicity so well it just clicks.

What s the last product that made you pause and think:

Damn this is clever ?

Bonus points if it s something small but brilliant. Curious to see what s been impressing you lately

I recently came across some news:

Claude for Financial Services officially launched in mid-July. It s designed specifically for the finance industry, integrating data from platforms like PitchBook, Morningstar, Snowflake, S&P Global, and Databricks to support market research, due diligence, and investment decisions. During early testing, Claude Opus 4 hit 83% accuracy on complex Excel tasks.

It really makes me question how much room there is left for AI Agent startups ----LLMs are getting better at handling more and more tasks on their own.
For an AI Agent to have long-term value, it must be able to understand, remember, and adapt to a user's evolving preferences and context ---- something LLMs still struggle with due to their limited memory and continuity.

Introduction

Most cloud platforms AWS, GCP, Azure are optimized for stateless web apps or short-lived serverless functions. But deploying long-lived, stateful LLM agents is another beast entirely. You need durability, resilience, and observability. When we tried to push our own multi-agent AI system to production, we hit walls with all the complex infrastructure work that not only took hours but unstable.

If you're applying, reply below with what you're building so we can cheer you on!

If you're doing a startup and not applying, why aren't you applying?

If you're applying, reply below with what you're building so we can cheer you on!

If you're doing a startup and not applying, why aren't you applying?

I ve been working on some AI projects recently things like scheduled agents, API responders, and multi-agent systems that need to run continuously. One of the biggest headaches I ve run into is deployment.

Most cloud platforms (AWS, GCP, etc.) are built for stateless apps or short-lived functions. But for long-running, stateful agents, the kind that need to persist data, auto-recover from crashes, and expose custom endpoints it gets surprisingly messy. I ve spent so much time setting up VMs, Docker configs, and recovery logic than actually writing agent behavior logic.